from PIL import Image
import numpy as np
import cv2
from face_parsing import FaceParsing


def init_face_parsing_model(
    resnet_path="./models/face-parse-bisent/resnet18-5c106cde.pth", face_model_pth="./models/face-parse-bisent/79999_iter.pth"
):
    fp_model = FaceParsing(resnet_path, face_model_pth)
    return fp_model


def get_crop_box(box, expand):
    x, y, x1, y1 = box
    x_c, y_c = (x + x1) // 2, (y + y1) // 2
    w, h = x1 - x, y1 - y
    s = int(max(w, h) // 2 * expand)
    crop_box = [x_c - s, y_c - s, x_c + s, y_c + s]
    return crop_box, s


def face_seg(image, fp_model):
    seg_image = fp_model(image)
    if seg_image is None:
        print("error, no person_segment")
        return None

    seg_image = seg_image.resize(image.size)
    return seg_image


def get_image(image, face, face_box, fp_model, upper_boundary_ratio=0.5, expand=1.2):
    # print(image.shape)
    # print(face.shape)

    body = Image.fromarray(image[:, :, ::-1])
    face = Image.fromarray(face[:, :, ::-1])

    x, y, x1, y1 = face_box
    # print(x1-x,y1-y)
    crop_box, s = get_crop_box(face_box, expand)
    x_s, y_s, x_e, y_e = crop_box
    face_position = (x, y)

    face_large = body.crop(crop_box)
    ori_shape = face_large.size

    mask_image = face_seg(face_large, fp_model)
    mask_small = mask_image.crop((x - x_s, y - y_s, x1 - x_s, y1 - y_s))
    mask_image = Image.new("L", ori_shape, 0)
    mask_image.paste(mask_small, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))

    # keep upper_boundary_ratio of talking area
    width, height = mask_image.size
    top_boundary = int(height * upper_boundary_ratio)
    modified_mask_image = Image.new("L", ori_shape, 0)
    modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))

    blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
    mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
    mask_image = Image.fromarray(mask_array)

    face_large.paste(face, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))
    body.paste(face_large, crop_box[:2], mask_image)
    body = np.array(body)
    return body[:, :, ::-1]


def get_image_prepare_material(image, face_box, fp_model, upper_boundary_ratio=0.5, expand=1.2):
    body = Image.fromarray(image[:, :, ::-1])

    x, y, x1, y1 = face_box
    # print(x1-x,y1-y)
    crop_box, s = get_crop_box(face_box, expand)
    x_s, y_s, x_e, y_e = crop_box

    face_large = body.crop(crop_box)
    ori_shape = face_large.size

    mask_image = face_seg(face_large, fp_model)
    mask_small = mask_image.crop((x - x_s, y - y_s, x1 - x_s, y1 - y_s))
    mask_image = Image.new("L", ori_shape, 0)
    mask_image.paste(mask_small, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))

    # keep upper_boundary_ratio of talking area
    width, height = mask_image.size
    top_boundary = int(height * upper_boundary_ratio)
    modified_mask_image = Image.new("L", ori_shape, 0)
    modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))

    blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
    mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
    return mask_array, crop_box


def get_image_blending(image, face, face_box, mask_array, crop_box):
    body = Image.fromarray(image[:, :, ::-1])
    face = Image.fromarray(face[:, :, ::-1])

    x, y, x1, y1 = face_box
    x_s, y_s, x_e, y_e = crop_box
    face_large = body.crop(crop_box)

    mask_image = Image.fromarray(mask_array)
    mask_image = mask_image.convert("L")
    face_large.paste(face, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))
    body.paste(face_large, crop_box[:2], mask_image)
    body = np.array(body)
    return body[:, :, ::-1]
